1. Field of the Invention
The present invention relates to fields of computer-assisted quantitative image analysis and methods to classify cells related to cancer progression. More specifically, it concerns methods of multivariate statistical analysis as applied to prediction of organ-confined disease status based upon the sextant biopsy pathology, PSA and quantitative image analysis. Also set forth is a method of predicting non-organ-confined disease status in patients based upon results of tests performed prior to election of any treatment or following such treatment.
2. Description of the Related Art
Prostate cancer is diagnosed in 100/100,000 white males and in 70.1/100,000 black males in the United States. It is the second leading cause of male cancer deaths and the most commonly diagnosed cancer in men in the United States representing 21% of all newly diagnosed cancers. In 1993 an estimated 165,000 men in the United States were diagnosed with clinically apparent prostate cancer and 35,000 will succumb to the disease. The age-specific increase in incidence achieves a maximum of 1000/100,000 in men &gt;75 years of age. The lifetime risk of developing clinical prostate cancer in the U.S. is 8.7% for white and 9.4% for black Americans with a lifetime risk of dying being 2.6% and 4.3% respectively. The risk of developing prostate cancer has risen 42.6% since 1975 as compared to an increase of only 26% in risk of developing lung cancer for that same time period. Approximately 65% of prostate cancers are clinically localized at the time of diagnosis and potentially curable with standard surgical techniques, yet only 50% of men are found to have disease confined to the prostate at the time of surgery. Pack and Spitz (Pack R. and Spitz M. A. The Cancer Bulletin, 45:384-388, 1993), reviewing the epidemiology of prostate cancer, indicated several definable risk factors such as age, race, dietary fat consumption, vasectomy, and familial aggregation with at least a two-fold increased risk for first generation relatives of men with prostate cancer (rare autosomal dominant inheritance). These causal correlations, though impressive, can not yet explain the complex etiology, biologic heterogeneity, and rapidly increasing incidence of this disease, and await further investigations of genetic, epigenetic and environmental factors.
The mortality rate for prostate cancer has been steadily increasing over the past 40 years and will continue to do so as our population ages. This clinically evident disease represents only the tip of the iceberg in that nearly 30 percent of all men over age 50 harbor a silent microscopic form of latent prostate cancer. Current early detection methods are increasing the numbers of this latent form of cancer identified, which now represent more than 11 million cases within the male population in the United States, and growth rate studies indicate that these tumors appear to grow very slowly and the great majority should remain clinically silent.
Recent advancements in transrectal ultrasonography and the development of a serum based assay (prostate specific antigen, PSA) for early detection has caused the diagnosis of premalignant neoplasias as well as prostate cancer to increase at an alarming rate. Many of these newly diagnosed neoplasias could represent the non-aggressive, potentially latent form of the disease that may never have become clinically evident if followed without therapy. Unfortunately, no accurate and specific methods presently exist to distinguish the more potentially aggressive form of prostate cancer from the latent form of the disease; thus most patients diagnosed are presently treated as though they had the aggressive form of the disease. At present, the factors to be considered in assessing cancer progression are estimates and significance of tumor volume, pre- and post-operative histological grading of cancer and high grade intraepithelial neoplasia, clinical and pathological stage, and serum prostate specific antigen (PSA) to predict biological aggressiveness of prostate cancer. These techniques Generally have only marginal predictive value.
It is well accepted that the epigenetic and genetic transformation of a normal prostatic epithelial cell to a cancer cell with progression to a metastatic phenotype requires multiple steps. The development of methods to quantify accurately these changes in order to better predict tumor aggressiveness has been the subject of much experimental work in prostate cancer. The use of chromatin texture feature data extracted from either H&E or Feulgen stained sections correlate well to classification of malignant cells. However, the sensitivity of Markovian texture measurements is complicated by the level of pixel gray level resolution (grain). Dawson et al. used a CAS-100 Image Analysis System and software to measure 22 Markovian texture features at 20 levels of pixel resolution (grain) and found ten features that discriminated chromatin patterns in breast cancer images captured by the CAS-100. Markovian analysis is a method based on determining gray-level transition probabilities and it allows discrimination among different nuclear texture features; the value for each feature depending on the level of grain resolution for each measurement.
Christen et al. applied a linear discriminant statistical model analysis of shape, size and texture features of H&E stained prostate nuclei to a high efficiency, 93% correct classification of normal and abnormal cells. Also, Irinopoulou et al. employed Feulgen stained nuclei and a computer-assisted image analysis system to characterize digitized images (512.times.512 pixels, with 256 possible gray tone levels) from twenty-three patients with Stage B carcinoma of the prostate followed for at least three years. Using five chromatin texture features and discriminant analysis methodology, these patients could be divided into those with a good and poor prognosis.
In spite of the progress made in predicting the organ confinement of prostate cancer cells, it is evident that improvements are needed in the accuracy of such determinations. A particular advantage would be realized by the development of methods that provide for accurate and reproducible statistical analysis of prognostic variables to maximize the aggregate positive predictive value while simultaneously reducing false negatives and false positives.